Real-time image quality is a critical requirement in a number of healthcare environments. Additionally, non-real-time applications, such as research and drug studies suffer loss of data due to unusable (untradeable) retinal images. Several published reports indicate that from 10% to 15% of images are rejected from studies due to image quality. With the transition of retinal photography to lesser trained individuals in clinics, image quality may suffer unless there is a means to assess the quality of an image in real-time and give the photographer recommendations for correcting technical errors in the acquisition of the photograph. In Phase I, this project demonstrated a methodology for evaluating a digital image from a funds camera in real-time and giving the operator feedback as to the quality of the image. We showed that it is possible to identify the source of the problem in poor quality images and give the photographer corrective actions. By providing real-time feedback to the photographer, corrective actions can be taken and loss of data or inconvenience to the patient eliminated. We successfully applied our methodology to over 2,000 images from four different cameras under mydriatic and non-mydriatic imaging conditions. We showed that the technique was equally effective on uncompressed and compressed (JPEG) images. In Phase II, we will validate the methodology further on additional data with different characteristics to demonstrate its broad applicability. Because our methodology uses parameters that are suggested by human perception qualities, we have shown that the algorithm can adapt to a variety of image quality protocols. The real- time retinal image quality methodology is based on image quality scores assigned by graders or ophthalmologists. Commercially, a real-time image quality assessment system is of interest to many manufacturers of fundus cameras. Our methodology will be demonstrated to be scalable to any digital imagery. We will integrate the algorithm into the image acquisition software of two commercial cameras (Topcon and Canon). Our methodology will also be of great value to screening centers where poor quality images can be reported immediately to the local or remote photographer. Commercially there will be three products: One, we will integrate the software directly into fundus cameras' image acquisition software. Two, we will produce a stand-alone image quality software package for use by individuals in clinics or research. Three, we will integrate our software and adapt it to specific protocols, such as the Wisconsin Fundus Photo Reading Center. 2